U.S. patent application number 11/342478 was filed with the patent office on 2006-09-14 for method for optimization of the resources of a medical facility and associated optimization system.
Invention is credited to Andreas Kuchler, Eva-Maria Reiter, Karola Wollstein.
Application Number | 20060206298 11/342478 |
Document ID | / |
Family ID | 36972136 |
Filed Date | 2006-09-14 |
United States Patent
Application |
20060206298 |
Kind Code |
A1 |
Kuchler; Andreas ; et
al. |
September 14, 2006 |
Method for optimization of the resources of a medical facility and
associated optimization system
Abstract
A method and associated optimization system make simple and fast
determination of possibilities to optimize a number of medical
facility resources comprising machine and personnel components
regarding their efficiency. Here, a simulation model is provided
numerically representing facility components, within which
simulation model a number of component-specific distinguishing
parameters are associated with each component and that moreover
comprises a number of superordinate distinguishing parameters. An
initialization parameter set is created via association of an
initial value with each distinguishing parameter. An objective
function of the distinguishing parameters is determined and at
least one distinguishing parameter selected from the superordinate
distinguishing parameters is defined as variable. The/every
variable distinguishing parameter is varied according to a
predetermined optimization algorithm with regard to a mathematical
optimization of an objective function. An optimized resource
configuration is recommended using an optimized parameter set in
which the objective function corresponds to a predetermined
optimization rule.
Inventors: |
Kuchler; Andreas; (Erlangen,
DE) ; Reiter; Eva-Maria; (Rottingen, DE) ;
Wollstein; Karola; (Erlangen, DE) |
Correspondence
Address: |
SCHIFF HARDIN, LLP;PATENT DEPARTMENT
6600 SEARS TOWER
CHICAGO
IL
60606-6473
US
|
Family ID: |
36972136 |
Appl. No.: |
11/342478 |
Filed: |
January 30, 2006 |
Current U.S.
Class: |
703/22 |
Current CPC
Class: |
G06Q 30/00 20130101;
G06Q 10/00 20130101 |
Class at
Publication: |
703/022 |
International
Class: |
G06F 9/45 20060101
G06F009/45 |
Foreign Application Data
Date |
Code |
Application Number |
Jan 28, 2005 |
DE |
10 2005 004 144.2 |
Claims
1. A method for optimizing resources of a medical facility, wherein
the resources comprise a number of machine components and personnel
components, comprising: forming a simulation model numerically
representing the components of the facility; associating, in the
simulation model, a number of component-specific distinguishing
parameters with each component, wherein the component-specific
distinguishing parameters relate to at least costs, uses,
utilization and performance of the associated components, wherein
said simulation model comprises a number of superordinate
distinguishing parameters that characterize at least a type and
number of the components considered in the simulation model as well
as relationships existing between various components; creating an
initialization parameter set via association of an initial value
with each distinguishing parameter; determining an objective
function of the distinguishing parameters; determining, as
variable, at least one distinguishing parameter selected from the
superordinate distinguishing parameters; varying the at least one
variable distinguishing parameter according to a predetermined
optimization algorithm with regard to a mathematical optimization
of objective function; and providing a recommendation for an
optimized resource configuration that is based on using an
optimized parameter set in which the objective function corresponds
to a predetermined optimization rule.
2. The method according to claim 1 wherein, the initial values for
the initial parameter set are acquired using existing components of
the facility (1).
3. The method according to claim 2, wherein the initial value of at
least one distinguishing parameter that characterizes the
utilization of a component is determined via automatic time
recording.
4. The method according to claim 2, wherein the initial value of at
least one distinguishing parameter that characterizes the use of a
component is automatically determined using stored accounting
rates.
5. The method according to claim 1, wherein the objective function
reflects the total costs or a total cost or total use ratio of the
components considered in the simulation model.
6. The method according to claim 1, wherein the forming of the
simulation model ensues utilizing the pre-configured distinguishing
parameter templates as building blocks, of which each
pre-configured distinguishing parameter template is stored with
regard to an available component type.
7. An optimization system for optimization of resources of a
medical facility, wherein the resources comprise: a number of
machine components; and personnel components; the optimization
system comprises: a model generation module that generates a
simulation model numerically representing the components of the
facility, within which simulation model a number of
component-specific distinguishing parameters are associated with
each component, which component-specific distinguishing parameters
characterize at least costs, usage, utilization and performance of
the associated components, the simulation model defining a number
of superordinate distinguishing parameters that characterize at
least a type and number of the components considered in the
simulation model as well as relationships existing between various
components; an input for assigning distinguishing parameters with
respectively one associated initial value; a calculation model that
varies a number of superordinate distinguishing parameters
determined as variable according to requirements of a mathematical
optimization of an objective function of the distinguishing
parameters; and an evaluation model that derives a recommendation
for an optimized resource configuration using an optimized
parameter set in which the objective function corresponds to a
predetermined optimization rule.
8. The optimization system according to claim 7, further
comprising: a storage model comprising one associated
distinguishing parameter template per available component type for
a number of available component types for generation of the
simulation model as a building block system.
Description
BACKGROUND
[0001] The invention concerns a method for optimization of the
resources of a medical facility, particularly of a clinic, a
department of such a clinic or the like. The invention furthermore
concerns an optimization system for implementation of the cited
method.
[0002] The resources of a medical facility are particularly
comprised of a number of machine components as well as a number of
personnel components. The term "resources" furthermore comprises
available consumable materials. The term "machine components" is to
be understood as a specific examination apparatus, for example, a
magnetic resonance (MR) tomography, a computer tomograph or a
specific computer system of the facility. A "personnel component"
is a specific employee, such as a doctor, a nurse or the like.
[0003] The resources of a medical facility exist in a complex
network of relationships and mutual dependencies with one another.
In particular, a number of employees are normally associated with
one examination apparatus. Furthermore, modern examination
apparatuses are frequently linked in a network of associated or
superordinate computer systems, for example, control computers,
finding stations, RIS/HIS components etc. Due to these
relationships, a component can only be effectively used when the
further components on which it depends are also available. A
computer tomograph, for example, is only effectively usable when
sufficient operating personnel and consumable material (such as
contrast agent, etc.) are available on the one hand and when, on
the other hand, the data processing network in which it is linked
functions.
[0004] In order to achieve an optimally effective workflow within
the facility in light of this background, automated organization
systems (what are known as "schedulers") are used to an increasing
degree that assign the present resources to assignments or tasks,
optimized (corresponding to the underlying relationship network)
for execution of a predetermined contingency. Such a scheduler,
however, always assumes the present resources and can therefore not
draw a conclusion as to whether the existing resource configuration
is inherently appropriate or whether an improvement with regard to
the efficiency of the facility could be achienved via a change of
the resources, be it via design, replacement, improvement or
disassembly of components or via an alteration of the relationship
network between the existing components.
[0005] In light of the complex relationship network of the resource
of a medical facility, such a conclusion can also only be estimated
with difficulty via rough observation of the operation of the
facility.
[0006] A method for determination of the
profitability/cost-effectiveness of a medical apparatus is known
from German patent document DE 101 36 238 A1.
SUMMARY
[0007] The invention is based on the object to provide a method by
which possibilities to improve the resources of a medical facility
with regard to their efficiency can be determined in a fast and
uncomplicated, and particularly an automated manner. The invention
is furthermore based on the object to provide an optimization
system particularly suitable for implementation of the cited
method.
[0008] With regard to the method, the object is inventively
achieved via a method for optimizing resources of a medical
facility, wherein the resources comprise a number of machine
components and personnel components, comprising: forming a
simulation model numerically representing the components of the
facility; associating, in the simulation model, a number of
component-specific distinguishing parameters with each component,
wherein the component-specific distinguishing parameters relate to
at least costs, uses, utilization and performance of the associated
components, wherein said simulation model comprises a number of
superordinate distinguishing parameters that characterize at least
a type and number of the components considered in the simulation
model as well as relationships existing between various components;
creating an initialization parameter set via association of an
initial value with each distinguishing parameter; determining an
objective function of the distinguishing parameters; determining,
as variable, at least one distinguishing parameter selected from
the superordinate distinguishing parameters; varying the at least
one variable distinguishing parameter according to a predetermined
optimization algorithm with regard to a mathematical optimization
of objective function; and providing a recommendation for an
optimized resource configuration that is based on using an
optimized parameter set in which the objective function corresponds
to a predetermined optimization rule.
[0009] The following illustrates the invention by discussion of
various embodiments of the invention.
[0010] It is accordingly provided to numerically represent, on a
simulation model, the resources of a medical facility that at least
comprise a number of machine and personnel components (i.e., at
least one machine component and at least one personnel component.
Furthermore, consumable goods assets are optionally recorded in the
simulation model as further components of the resources.
[0011] The simulation model comprises a number of distinguishing
parameters. Differences are hereby component-specific
distinguishing parameters that are associated with a specific
component of the facility and superordinate distinguishing
parameters. The (component-specific) distinguishing parameters
associated with a component generally contain at least
specifications regarding the costs, the uses, the utilization and
the capacities of the respective component.
[0012] With regard to a machine component, one or more cost-related
distinguishing parameters are provided via which, for example,
acquisition costs and/or operating costs as well as average repair
costs of the component are accounted for. In particular retention
costs are considered with regard to a personnel component.
[0013] The revenues achieved via the use of the respective
components are recorded as uses in one or more corresponding
usage-related distinguishing parameters. For example, these
distinguishing parameters hereby comprise absolute values of the
revenues achieved with the component in an observation time span
or, however, relative values (such as billing rates) from which the
absolute usage can be determined using the utilization of the
component.
[0014] In the simplest case, a simpler percentage rate is provided
for specifying the utilization of a component, which percentage
specifies to what extent a specific component is productively used.
As an alternative to this, the utilization of a component in a
differentiated form is recorded by the distinguishing parameters.
In particular, the number and type of the medical examinations
effected by the component is recorded as a measure for the
utilization of a component, etc.
[0015] The capacity of a component is recorded within the
simulation model via distinguishing parameters that describe the
technical performance of a machine component (for example, set-up
time, shut-down time, average patient residence times, etc.) For a
personnel component, e.g., the work time, the level of education,
the treatments that can be implemented by the employee and/or the
apparatuses that can be operated by the employee are recorded as
specifications regarding productivity. With regard to the capacity,
distinguishing parameters are optionally also recorded that
characterize the minimum requirements of a component. The
distinguishing parameters regarding a machine component thus
appropriately contain specifications regarding the number and type
of the personnel components that at the least must be available in
the operation of the machine component. For a specific computer
tomograph, for example, the associated distinguishing parameters
contain specifications regarding a minimum number of radiologists,
assistants, etc., to be associated.
[0016] The type and number of the considered components as well as
the relationships existing between the components are recorded
within the simulation model via the superordinate distinguishing
parameters. In particular, associations of a component with a
further component and/or the spatial distance between two machine
components etc. are recorded.
[0017] The simulation model is initially initialized in the course
of the optimization method. In other words, initial values are
assigned to the distinguishing parameters of the simulation model
and an initial parameter set for the optimization process is thus
formed. The initial values are preferably determined using the
actual conditions of the underlying facility. However, as needed,
initial values deviating from these can also be specified,
particularly in order to be able to simulatively "act out" specific
virtual resource configurations.
[0018] Furthermore, using the simulation model, an objective
function of the distinguishing parameters is determined according
to the requirements of which the resources should be optimized. The
total costs or the total cost/total usage ratio of the components
considered in the simulation model are thereby preferably used as
an objective function. Alternatively, for example, the total time,
the patient throughput, etc., required for implementation of a
predetermined contingency of tasks or assignments can also be
determined as an objective function.
[0019] A number of superordinate distinguishing parameters (i.e.,
at least one such distinguishing parameter) are initially
determined as variable for the actual optimization process. The
variable distinguishing parameter or distinguishing parameters are
now varied via a predetermined optimization algorithm until an
established mathematical optimization rule is fulfilled with regard
to the objective function. It is optionally provided that
component-specific parameters can also additionally be determined
as variable. The optimization algorithm is, in particular, a
mathematical extreme value search in which the variable
distinguishing parameters are varied until a minimal value or
maximal value of the objective function is achieved. In the case of
a total cost/total usage ratio as an objective function, for
example, a minimum is appropriately sought; in the case of the
patient throughput, a local or global maximum is appropriately
sought as an objective function.
[0020] Alternatively, for example, it can also be provided as an
optimization rule that the variable distinguishing parameters are
varied until the function value of the objective function has
reduced or increased by a predetermined percentage relative to the
initial value.
[0021] The optimization algorithm is, in particular, a numerical,
and particularly an iterative mathematical regression method.
Depending on the properties of the simulation model, a
deterministic or stochastic method (which can be a conventional
method) is alternatively also used as an optimization algorithm. In
simple cases, the mathematical equation describing the simulation
model can also be achieved via mathematical analytic
optimization.
[0022] If an optimized parameter set is determined in which the
objective function fulfills the optimization rule, a recommendation
for an optimized resource configuration is derived from this.
Relative to the original resources, the recommendation can
particularly provide for the addition of a new component, the
disposal, or the replacement of an existing component, or a
revaluation of a component. The recommendation can furthermore, for
example, provide for a change of the dependencies present between
two existing components.
[0023] Via the method described in the preceding, possibilities to
improve the efficiency of the resources of a medical facility are
indicated in a simple and fast manner that is uncomplicated with
regard to personnel expenditure. The method is preferably
implemented completely or largely automatically, in which, however,
manual intervention in the method implementation can also be made
as needed. For example, the effect of any intended resource
alteration can be simulated without risk beforehand in this
manner.
[0024] With regard to an automated (and therewith simplified)
method implementation, it is preferably provided that the initial
value of at least one utilization-related distinguishing parameter
of a component is determined via automatic time recording. Such a
time recording is frequently already provided innately in the
course of organization and information systems such as a RIS
(radiology information system) and an HIS (hospital information
system) used in the medical field, such that the utilization data
can be directly accessed in the course of the present method.
[0025] For usage-related distinguishing parameters, associated
initial values are preferably automatically determined from a
stored accounting table, if applicable, under additional
consideration of the utilization of the component.
[0026] Machine distinguishing parameters are particularly provided
by the manufacturer of the respective apparatus in the form of data
sheets.
[0027] In a particularly advantageous embodiment of the method, the
simulation model is composed of pre-configured distinguishing
parameter templates, patterns, samples or models like building
blocks. Such a distinguishing parameter template is respectively
stored for a plurality of available component types. Each
distinguishing parameter template defines the relevant
component-specific distinguishing parameter for the respective
component type.
[0028] Analogous to the resources of the real facility that is
modularly comprised of its individual components, the simulation
model is comprised of instances of the distinguishing parameter
templates corresponding to these components. The simulation model
can be simply and modularly generated in this manner with regard to
a virtually arbitrary resource configuration to be simulated and
likewise simply be changed as needed.
[0029] With regard to the optimization system provided for
implementation of the method described in the preceding, the object
is inventively achieved via an optimization system for optimization
of resources of a medical facility, wherein the resources comprise
a number of machine components; and personnel components; the
optimization system comprising: a model generation module that
generates a simulation model numerically representing the
components of the facility, within which simulation model a number
of component-specific distinguishing parameters are associated with
each component, which component-specific distinguishing parameters
characterize at least costs, usage, utilization and performance of
the associated components, the simulation model defining a number
of superordinate distinguishing parameters that characterize at
least a type and number of the components considered in the
simulation model as well as relationships existing between various
components; an input for assigning distinguishing parameters with
respectively one associated initial value; a calculation model that
varies a number of superordinate distinguishing parameters
determined as variable according to requirements of a mathematical
optimization of an objective function of the distinguishing
parameters; and an evaluation model that derives a recommendation
for an optimized resource configuration using an optimized
parameter set in which the objective function corresponds to a
predetermined optimization rule.
[0030] Thus, the optimization system comprises a model generation
module that is fashioned for generation of the simulation model as
well as an input by way of which corresponding initial values can
be assigned to distinguishing parameters of the simulation model.
The input preferably comprise a statistical model that provides
(from statistical detection of the work processes running in the
real facility) initial values for utilization-related
distinguishing parameters, a cost databank that provides the
initial values for cost-related distinguishing parameters and an
accounting databank that provides accounting rates as an initial
value for usage-related distinguishing parameters.
[0031] The optimization system furthermore comprises a calculation
module in which the optimization algorithm described in the
preceding is implemented as well as an evaluation module that is
fashioned to derive the recommendation for an optimized resource
configuration using the optimized parameter set and to output this
recommendation for acceptance or rejection.
[0032] In a preferred embodiment, the optimization system
furthermore comprise a storage module as a template library for the
distinguishing parameter templates described in the preceding.
DESCRIPTION OF THE DRAWINGS
[0033] An exemplary embodiment of the invention using a drawing is
subsequently explained in detail. The single FIGURE shown therein
is a block diagram that schematically simplifies a medical facility
as well as an optimization system for optimization of the resources
of the facility.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0034] The exemplary shown medical facility 1, according to an
embodiment of the invention, is, for example, a radiology
department of a clinic. The facility 1 comprises a specific
configuration of resources 2 represented as pictograms. The
resources 2 comprise a number of machine components 3, personnel
components 4 (i.e., employees) as well as (material) components 5
(i.e., consumable good assets).
[0035] The machine components 3 are examination or therapy
apparatuses, computer systems, etc. In detail, the resources 2
comprises a magnetic resonance tomograph as component 3a, a
computer tomograph as component 3b and a C-arm x-ray apparatus as
component 3c. Further machine components 3d through 3i are
electronic optimization, information or evaluation systems, in
particular RIS or PACS components. Further machine components 3h
and 3i are what are known as "finding stations" for medical
assessment of digital examination data.
[0036] Personnel components 4 are, in particular, assistants
(components 4a and 4b), radiologists (components 4e and 4f), etc.
In detail, the resources 2 comprise personnel components 4a-4f. The
resources 2 additionally individually comprise three material
components 5a-5c.
[0037] A network of (partially mutual) relationships 6 exists
between the individual components 3, 4, 5. For example, the
personnel components 4a, 4b, the material component 5a and the
machine component 3d are associated with the component 3a, i.e.,
with the MR tomograph. For example, these relationships manifest
themselves in that the MR tomograph (component 3a) can only be
operated when the personnel components 4a and 4b and the material
component 5a are available, etc.
[0038] The facility 1 furthermore comprises an organization system
7, particularly a conventional scheduler, that is fashioned to use
the resources 2 optimized for implementation of incoming tasks A or
assignments. For this, the optimization system 7 receives
information I about the available components 3, 4, 5 of the
resources 2 and returns corresponding assignment instructions S to
the components 3, 4, 5.
[0039] An optimization system is furthermore associated with the
facility 1, which optimization system is particularly a component
of a data processing system and which is fashioned to determine,
using a simulation model 11 of the resources 2, a resource
configuration optimized with regard to the efficiency of the
facility 1.
[0040] For this, the optimization system 10 comprises a model
generation module 12 that is fashioned to generate the simulation
model 11 or--in as much as the simulation model 11 already
exists--to modify the simulation model 11. The simulation model 11
itself comprises a number of component-specific distinguishing
parameters x.sub.i (i=1, 2, . . . ) as well as a number of
superordinate distinguishing parameters X.sub.j (=1, 2, . . . )
that numerically represent the properties of the resources 2.
[0041] Each component-specific parameter x.sub.i hereby stands for
one property that is associated with a specific component 3, 4, 5.
In general, the distinguishing parameters x.sub.i associated with a
specific component 3, 4, 5 comprise specifications that
characterize costs, uses, utilization, and capacity of these
components 3, 4, 5.
[0042] Particularly, one distinguishing parameter x.sub.i is
provided per component 3, 4, 5, which one distinguishing parameter
x.sub.i represents the monthly costs to be estimated for the
corresponding component within a monitoring time span (preferably
monthly). For a machine component 3, investment costs, renting or
leasing costs, operating costs, and (if applicable) average repair
costs are considered. The incident salary costs are considered for
a personnel component. The acquisition costs are considered for a
material component 5.
[0043] Usage-related distinguishing parameters x.sub.i concern
accounting costs that can be accounted for given use of the
corresponding components 3, 4, 5. In particular, given machine
components 3 and personnel components 4, accounting rates
differentiated according to medical examination types are hereby
incorporated as distinguishing parameters x.sub.i.
[0044] The number of the examinations of a specific type that are
implemented by the corresponding components 3, 4 per observation
time span, differentiated for machine components 3 and personnel
components 4, are recorded as utilization-related distinguishing
parameters x.sub.i. The consumption per observation time span,
measured at the average inventory, is recorded as a
utilization-related distinguishing parameter x.sub.i for a material
component 5.
[0045] In particular, specifications regarding start-up times,
shut-down times, and patient residence times, differentiated
according to medical examination types, are recorded as
performance-related distinguishing parameters x.sub.i for a machine
component 3a through 3i. In particular, work times and treatment
duration, differentiated according to treatment types, are recorded
in this regard for a personnel component 4a through 4f.
[0046] Superordinate distinguishing parameters X.sub.j specify the
number and type of the components 3, 4, 5 numerically considered in
the simulation model 11. The superordinate distinguishing
parameters X.sub.j additionally numerically represent the
relationships 6 existing between the components 3, 4, 5, in that
they specify associations between various components 3, 4, 5
etc.
[0047] In the course of the optimization method implemented by way
of the optimization system 10, the simulation model 11 is initially
generated using information I' about the existing components 3, 4,
5 and their relationships 6 such that the simulation model 11
numerically represents real resources 2 of the facility 1. The
information I' can be partially or completely automatically
supplied to the simulation model or be input manually.
[0048] The model generation module 12 generates the simulation
model 11 according to the building block principle using
distinguishing parameter templates V.sub.k (k=1, 2, . . . ) that
are provided to the model generation module 12 from a storage model
(subsequently designated as a template library 13). Each
distinguishing parameter template V.sub.k is specifically for a
specific component type T.sub.k (k=1, 2, . . . ) that is in
principle provided as a component of the resources 2. The component
types T.sub.k particularly comprise types of employees of various
degrees of education, examination apparatuses (such as MR
tomographs or computer tomographs) of various types, various
computer systems and consumable materials. Similar components with
different financing models (for example, a purchased apparatus
relative to a corresponding leased apparatus, a salaried employee
relative to a comparable temporary employee, etc.) are also
optionally considered in different component types T.sub.k. Each
distinguishing parameter template V.sub.k defines the
component-specific parameter x.sub.i relevant for the respective
component type T.sub.k.
[0049] The components 3a-3i, 4a-4f, 5a-c forming the actual
resources of the facility 1 are instances of a respectively
associated component type T.sub.k, thus respectively represent a
concrete exemplar of the associated component type T.sub.k. For
example, the component 4a is a specific assistant and thus
corresponding to the component type T.sub.1; the component 3a
likewise corresponds to a component type T.sub.k focused on a
specific MR tomograph, etc.
[0050] For each component 3a-i, 4a-f, 5a-c, the model generation
module 12 corresponding selects from the template library 13 the
distinguishing parameter template V.sub.k corresponding to the
component type T.sub.k and adds this distinguishing parameter
template V.sub.k to the simulation model 11. The finished
simulation model 11 thus contains a corresponding instance of the
respective distinguishing parameter template V.sub.k for every
component 3a-i, 4a-f, 5a-c.
[0051] In a next step, the simulation model 11 is initialized,
i.e., it is provided that an associated initial value W is
allocated to each of the distinguishing parameters x.sub.i,
X.sub.j. Initial values W for cost-related distinguishing
parameters x.sub.i are provided from a cost databank 14; initial
values W for usage-related distinguishing parameters x.sub.i are
provided from an accounting databank 15 in which accounting rates
(if applicable, differentiated according to examination type,
component type T.sub.k, health insurance, etc.) are stored. Initial
values W regarding utilization-related distinguishing parameters
x.sub.i are provided to the simulation model 11 from a statistical
model 16 that (using the classification instructions S of the
organization system 7) statistically records the utilization of the
components 3, 4, 5, i.e., particularly the number and type of the
examinations implemented per component as well as optional
non-productive times of individual components 3, 4, 5 and/or
patient wait times.
[0052] Performance-related distinguishing parameters x.sub.i that
are normally invariable for a specific component type T.sub.k are
preferably already recorded as constants beforehand in the stored
distinguishing parameter templates V.sub.k and inasmuch are already
allocated with the corresponding initial value W. The superordinate
distinguishing parameters X.sub.j are allocated with initial values
W that are determined using the information I' about the existing
resources 2.
[0053] For a mathematical optimization of the simulation model 11,
the optimization system 10 furthermore comprises a calculation
model 17. An optimization algorithm 18 is implemented in the
calculation model 17; an objective function F=F(X.sub.j, x.sub.i)
of the distinguishing parameters X.sub.j and x.sub.i as well as an
optimization rule R are provided for the optimization algorithm 18.
The objective function F is generally a mathematical rule,
represented using the simulation model 11, that reproduces
(dependent on the distinguishing parameters X.sub.j and x.sub.i) a
function value according to the requirements of which the
optimization algorithm 18 measures the progress of the optimization
process. In particular, the cost-usage ratio is drawn upon as an
objective function F, which cost-usage ratio results from the
simulation model 11 under consideration of the supplied utilization
data.
[0054] The optimization rule R provides a criterion for the success
of an optimization process implemented by the optimization
algorithm 18. For example, the optimization rule R contains the
instruction to determine a minimum of the cost-usage ratio as an
objective function F with a predetermined precision. A number of
setting parameters X.sub.l (l.epsilon.j) from the superordinate
distinguishing parameters X.sub.j that should be handled as
variable in the course of the optimization process are furthermore
provided to the optimization algorithm 18. An arbitrary subset of
the superordinate distinguishing parameters X.sub.j that comprise
at least one distinguishing parameter X.sub.l can be selected as
variable. It is optionally provided that component-specific
distinguishing parameters x.sub.i can also be selected as
variable.
[0055] The optimization algorithm 18 can be a numerical regression
method. At the beginning of the optimization process, the
distinguishing parameters x.sub.i, X.sub.j assigned with the
initial values W are supplied to the optimization algorithm 18 as
an initial parameter set P.sub.ini. The optimization algorithm
varies one or more of the variable distinguishing parameters
X.sub.l such that the objective function F is positively influenced
with regard to the optimization rule R and returns the changed
values of the distinguishing parameters X.sub.l to the model
generation module 12, which reconstructs the simulation model 11
under consideration of the variations.
[0056] Insofar as the optimization algorithm 18 is iteratively
designed, the distinguishing parameters x.sub.i, X.sub.j of the
changed simulation model 11 together with the associated values are
in turn supplied to the calculation module 17, which re-modifies
the values of the variable distinguishing parameters X.sub.l and in
turn returns these to the model generation module 12. This process
is repeated until the optimization rule R is fulfilled,
particularly until a minimum of the cost-usage ratio as an
objective function F is determined with the specified
precision.
[0057] Via the release of superordinate distinguishing parameters
X.sub.l as variables, in the course of the optimization process,
the simulation model 11 is modified relative to the initial,
fundamental configuration of the resources 2. In particular,
virtual new components can be added to the simulation model 11 or
existing components can be removed or replaced via changing of the
corresponding distinguishing parameters X.sub.l. Additionally, the
relationships and dependencies specified between the virtual
components of the simulation model 11 can be altered relative to
the relationships of the real components 3a-3i, 4a-4f, 5a-5c. In
particular, the association of a personnel component with a machine
component can be changed etc. The method determines, using the
objective function F, to what extent a concrete change of the
resource configuration for optimization of the objective function
is reasonable, particularly to what extent it leads to a cost
savings.
[0058] Given fulfillment of the optimization rule R, the
calculation model 17 terminates the optimization process and leaves
the simulation model 11 in an end state described by an optimized
parameter set P.sub.opt of the distinguishing parameters x.sub.i,
X.sub.j and their values. Via a corresponding instruction C, the
calculation model 17 now activates an evaluation module 19 that
derives a recommendation B for an optimized resource configuration
of the facility 1 using the parameter set P.sub.opt. The
recommendation B preferably has the form of a written report in
which are indicated the optimized resource configuration and in
particular its difference relative to the existing resources 2 of
the facility 1 as well as the influence of the proposed
optimization on the objective function F. The proposal B is output
to a user 21 by the evaluation module 19 on a control console 20 of
the optimization system 10 comprising an input and output such as a
screen, keyboard, mouse, etc. The recommendation B can also be
output in an equivalent manner in paper form, as e-mail, or in a
comparable manner. The user can now accept or dismiss the
recommendation B, whereby, in the event of an acceptance, the
change of the resources 2 of the facility 1 that is offered by the
recommendation B is automatically or manually implemented.
[0059] The acceptance process set in motion by the evaluation
module 19 is optionally effected in a differentiated manner (in a
manner not shown in detail), in that partial recommendations that
respectively contain changes for a partial range of the resources 2
are transmitted to various users responsible for the respective
partial range. For example, alteration recommendations for the
personnel component 4 of the resources 2--for example, an advanced
training of the radiologist (component 4e), the employment of an
additional assistant for the computer tomograph (component 3b), or
a change of the association of the present personnel with the
present examination apparatuses (components 3a through 3c)--are
forwarded to a personnel department of the facility 1, while change
recommendations with regard to the machine components 4 are
forwarded to an administration position of the facility 1 that is
responsible for this. If the recommendation B concerns both changes
to personnel components 4 and changes to machine components 3, the
evaluation module 19 thus only initiates the implementation of the
recommendation B when all responsible positions of the facility 1
have accepted the respective partial recommendations.
[0060] The method described in the preceding is alternately
implemented at the corresponding initiation by a user or
automatically at regular time intervals, particularly monthly.
Details of the method implementation can be amended as needed,
specific to the user. In particular, distinguishing parameters
x.sub.i, X.sub.j of the simulation model 11 or initial values W for
the optimization process that deviate from the actual conditions of
the facility 1 can be provided. This is particularly reasonable in
order to be able to "act out" specific scenarios virtually (and
thus without risk). The objective function F, the optimization rule
R and the variable distinguishing parameters X.sub.l are preferably
freely configurable for this purpose.
[0061] However, with regard to the selection of the objective
function F, the optimization rule R and the variable distinguishing
parameters X.sub.l specific, advantageous defaults can
alternatively also be provided in order to simplify the handling of
the optimization system 10.
[0062] For the purposes of promoting an understanding of the
principles of the invention, reference has been made to the
preferred embodiments illustrated in the drawings, and specific
language has been used to describe these embodiments. However, no
limitation of the scope of the invention is intended by this
specific language, and the invention should be construed to
encompass all embodiments that would normally occur to one of
ordinary skill in the art.
[0063] The present invention may be described in terms of
functional block components and various processing steps. Such
functional blocks may be realized by any number of hardware and/or
software components configured to perform the specified functions.
For example, the present invention may employ various integrated
circuit components, e.g., memory elements, processing elements,
logic elements, look-up tables, and the like, which may carry out a
variety of functions under the control of one or more
microprocessors or other control devices. Similarly, where the
elements of the present invention are implemented using software
programming or software elements the invention may be implemented
with any programming or scripting language such as C, C++, Java,
assembler, or the like, with the various algorithms being
implemented with any combination of data structures, objects,
processes, routines or other programming elements. Furthermore, the
present invention could employ any number of conventional
techniques for electronics configuration, signal processing and/or
control, data processing and the like.
[0064] The particular implementations shown and described herein
are illustrative examples of the invention and are not intended to
otherwise limit the scope of the invention in any way. For the sake
of brevity, conventional electronics, control systems, software
development and other functional aspects of the systems (and
components of the individual operating components of the systems)
may not be described in detail. Furthermore, the connecting lines,
or connectors shown in the various figures presented are intended
to represent exemplary functional relationships and/or physical or
logical couplings between the various elements. It should be noted
that many alternative or additional functional relationships,
physical connections or logical connections may be present in a
practical device. Moreover, no item or component is essential to
the practice of the invention unless the element is specifically
described as "essential" or "critical". Numerous modifications and
adaptations will be readily apparent to those skilled in this art
without departing from the spirit and scope of the present
invention.
* * * * *